جست‌وجوی استاد و پژوهشگر

Jeffrey Sanford Russellمشاهده پروفایل

استاد

• Decision Theory
• Ethics
• Formal Epistemology
• +۴ مورد دیگر

Jeffrey Sanford Russell is a Professor of Philosophy at the University of Southern California's School of Philosophy, where he also serves as Director of Graduate Studies. He holds a PhD from New York University (2011). His research focuses on decision theory, ethics, formal epistemology, metaphysics, and the philosophy of religion and mathematics. Russell teaches courses on topics like probability, rational choice, and the limits of logic, emphasizing interdisciplinary approaches blending philosophy with mathematics, psychology, and economics. His work explores foundational questions in ethics, such as infinite value aggregation paradoxes, and decision theory, including stochastic dominance and self-locating evidence in cosmology. He has contributed to debates on multiverse hypotheses, moral hedging, and the structure of possible worlds. Russell’s publications appear in top journals like Noûs , Philosophical Review , and Philosophy and Phenomenological Research . His recent research addresses moral and epistemic challenges in infinite scenarios, such as infinite ethics and fanaticism in decision-making. He also investigates the intersection of metaphysics and formal logic, including composition principles and qualitative grounds for non-qualitative facts. Russell’s work bridges abstract philosophical inquiry with practical implications, such as ethical dilemmas in collective action and the epistemology of divine hiddenness.

Scott Armstrongمشاهده پروفایل

استاد

• Partial Differential Equations
• Calculus of Variations
• Probability Theory
• +۳ مورد دیگر

Scott Armstrong is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on partial differential equations, calculus of variations, and probability theory, with a specialization in stochastic homogenization of PDEs in random media and related statistical mechanical systems. He holds a Ph.D. from UC Berkeley (2009) and a B.S. from Texas A&M University (2002). Education: Ph.D. in Mathematics, University of California, Berkeley, USA (2009) B.S. in Mathematics, Texas A&M University, USA (2002) Research Interests: Scott's work addresses fundamental questions in homogenization theory, including quantitative estimates for elliptic and parabolic equations in random media, renormalization group methods, and applications to statistical mechanics. His contributions bridge analysis, probability, and mathematical physics, with a focus on rigorous mathematical frameworks for understanding macroscopic behavior from microscopic models. Publications: His recent work includes studies on anomalous diffusion, renormalization group techniques, and quantitative homogenization in high-contrast media. Over 50 peer-reviewed articles highlight his expertise in stochastic PDEs, elliptic regularity, and variational methods. Awards: No specific awards listed in the provided text. Advising & Grants: No student advisees or grant details explicitly mentioned in the text. Labs/Teams: No dedicated labs or collaborative teams explicitly noted, though his research likely involves interdisciplinary collaborations within the Courant Institute.

Tianyi Linمشاهده پروفایل

استادیار

• Optimization
• Game Theory
• Machine Learning
• +۳ مورد دیگر

Tianyi Lin serves as an Assistant Professor in the Department of Industrial Engineering and Operations Research (IEOR) at Columbia Engineering, Columbia University, a position he assumed in 2024. He holds dual affiliations as a verified Data Science Institute (DSI) Member and an Affiliated Member of both the Financial and Business Analytics Center and the Foundations of Data Science Center. His academic credentials include: Ph.D. in Electrical Engineering and Computer Science, UC Berkeley Postdoctoral Researcher, Laboratory for Information & Decision Systems (LIDS), MIT (2023-2024) M.S. in Operations Research, UC Berkeley M.S. in Pure Mathematics and Statistics, University of Cambridge B.S. in Mathematics, Nanjing University Dr. Lin's research spans optimization theory , game-theoretic models , and machine learning algorithms , with emphasis on nonconvex minimax problems , variational inequalities , and data science applications . His work bridges theoretical guarantees with practical implementations in high-dimensional settings, particularly focusing on convergence properties and computational efficiency in complex systems. Analysis of his 15 most recent publications (2022-2025) reveals dominant themes in high-order optimization methods , no-regret learning in games , and optimal transport algorithms . His contributions demonstrate consistent innovation in developing doubly optimal algorithms for monotone games, spectral regularization techniques for policy optimization, and structure-driven approaches for nonconvex problems, reflecting strong interdisciplinary connections between operations research, computer science, and applied mathematics. No scientific awards or honors were documented in the provided source material. Information regarding student advising and research grants remains unspecified in the current documentation, though his center affiliations suggest active participation in collaborative research initiatives. Dr. Lin maintains significant interdisciplinary engagement through his affiliations with Columbia's Data Science Institute and specialized research centers, positioning his work at the intersection of theoretical optimization and real-world data science applications.

Stefan Glockمشاهده پروفایل

استادیار

• Discrete Mathematics
• Extremal Combinatorics
• Probabilistic Combinatorics
• +۴ مورد دیگر

Professor Stefan Glock is an Assistant Professor of Discrete Mathematics at the University of Passau's Faculty of Computer Science and Mathematics, a position he has held since September 2022. Prior to this appointment, he spent three years as a Junior Fellow at the Institute for Theoretical Studies at ETH Zurich, following the completion of his doctorate at the University of Birmingham. Stefan Glock received his mathematics education at Technische Universität Ilmenau from 2009 to 2014, then pursued his PhD at the University of Birmingham, which he completed in 2018. His doctoral dissertation, "Decompositions of Graphs and Hypergraphs," was the runner-up for the Richard-Rado-Preis 2018. Professor Glock's research focuses on discrete mathematical structures, with particular emphasis on their asymptotic properties. His work spans several interconnected fields of combinatorics: Extremal Combinatorics : Investigating the maximum or minimum possible size of mathematical structures satisfying certain properties Probabilistic Combinatorics : Applying probability theory to solve combinatorial problems Graph Theory : Studying properties of graphs and networks Ramsey Theory : Examining conditions under which order must appear in large structures Design Theory : Creating arrangements of elements satisfying specific balance properties Discrete Geometry : Analyzing geometric problems with discrete structures Analysis of Professor Glock's recent publications reveals a consistent focus on solving long-standing open problems in combinatorics using innovative methods that combine probabilistic techniques with structural insights. His work often bridges theoretical mathematics with applications in theoretical computer science, particularly in the analysis of algorithms and network structures. A significant portion of his research addresses fundamental questions about graph and hypergraph decompositions, which have implications for coding theory, cryptography, and network design. Professor Glock has received notable recognition for his contributions to mathematics: Runner-up for the Richard-Rado-Preis 2018 for his dissertation "Decompositions of Graphs and Hypergraphs" Awarded funding through the prestigious DFG Emmy Noether Programme in 2024 for his research group on "the interplay of structure and randomness in mathematics" As a faculty member at the University of Passau, Professor Glock leads the Discrete Mathematics research group and actively collaborates with mathematicians worldwide. He has established a strong research program that has attracted funding for academic visitors and supports multiple research projects. His approach to mathematical problems emphasizes developing new methods that have far-reaching implications beyond the specific problems being solved. Professor Glock's research group at the University of Passau focuses on the interplay between structure and randomness in discrete mathematics. The group maintains active collaborations with leading institutions including ETH Zurich, University of Birmingham, and various research centers across Europe. Through the DFG Emmy Noether Programme funding, his group is expanding its research on combinatorial structures and their applications.

Debmalya Panigrahiمشاهده پروفایل

استاد

• Theoretical Computer Science
• Design and Analysis of Algorithms
• Graph Algorithms
• +۶ مورد دیگر

Debmalya Panigrahi is a Professor of Computer Science at Duke University and serves as Associate Chair in the Department of Computer Science since 2025. He received his Ph.D. in Theoretical Computer Science from the Massachusetts Institute of Technology (MIT), advised by David Karger, and also studied at the Indian Institute of Science and Jadavpur University. Ph.D., MIT, 2012 Indian Institute of Science Jadavpur University His research focuses on algorithms design and analysis, particularly in graph algorithms (minimum cuts, vertex connectivity, max-flows) and algorithms under uncertainty (online algorithms, learning-augmented frameworks). He also works on approximation algorithms, algorithmic game theory, and practical applications in advertising, AI, and network design. Recent publications address problems like network unreliability estimation, convex paging with fairness constraints, and hypergraph reliability. His work combines theoretical rigor with practical impact, including patents and prototypes. NSF CAREER Award He has received grants from the National Science Foundation (including multi-objective optimization projects), Google Inc., and the Indo-US Science and Technology Forum. He mentors graduate and undergraduate students, including current PhD candidates Ruoxu Cen and Anish Hebbar. Panigrahi is affiliated with Duke's theory group and collaborates with CS-econ, AI/ML, and database groups. He recently returned from a sabbatical at Berkeley (Simons Institute and UC Berkeley) and maintains strong ties with industry through roles at Google Research and Microsoft Research.

Lukasz Szpruchمشاهده پروفایل

استاد

• Probability Theory
• Stochastic Analysis
• Theoretical Machine Learning
• +۱۰ مورد دیگر

Lukasz Szpruch serves as Professor at the University of Edinburgh's School of Mathematics and Programme Director for Finance and Economics at The Alan Turing Institute. He leads the FAIR research programme on responsible AI adoption in financial services and co-investigates the UK Centre for Greening Finance & Investment (CGFI), directing partnerships with the National Office for Statistics, Accenture, Bill & Melinda Gates Foundation, and HSBC. He maintains affiliations with the Oxford-Man Institute for Quantitative Finance. His research focuses on probability theory , stochastic analysis , and theoretical machine learning , with current investigations into deep learning foundations, mean-field models, reinforcement learning, game theory, multiagent systems, and computational optimal transport. These theoretical frameworks are rigorously applied to financial economics problems including market dynamics, risk modeling, and regulatory compliance, emphasizing mathematical precision in AI system design. Recent publications reveal a strategic shift toward responsible AI deployment in finance , addressing large language model governance, synthetic data privacy, and non-asymptotic sampling theory. His work consistently bridges abstract mathematics with financial sector applications, particularly through the FAIR programme's industry collaborations that translate theoretical advances into practical frameworks for trustworthy AI adoption. As Principal Investigator of FAIR and CGFI co-Investigator, Szpruch manages significant research funding streams focused on AI ethics in financial services and sustainable finance. His academic leadership drives cross-sector initiatives where theoretical research directly informs regulatory policy development and industry best practices, though specific student mentoring details remain unspecified in source materials. Szpruch operates at the nexus of three critical research ecosystems: the FAIR programme's industry partnerships, CGFI's sustainability-focused finance research, and the Oxford-Man Institute's quantitative finance initiatives. These interconnected teams combine mathematical rigor with real-world financial applications, developing frameworks for AI assurance, green finance metrics, and synthetic data validation that address systemic challenges in modern financial systems.

• Corporate Finance
• Corporate Social Responsibility
• Happiness Economics
• +۳ مورد دیگر

Leonardo Becchetti is a Full Professor at the University of Rome Tor Vergata's Department of Economics and Finance, teaching Microeconomics and Topics in Applied Economics for the 2025-2026 academic year. Based in room P2 S55, he maintains an active research profile with email contact becchetti@economia.uniroma2.it. His research centers on Corporate Finance, Corporate Social Responsibility, and Happiness Economics, extending to Sustainable Development and Ecological Transition. He investigates how economic structures influence well-being through relational goods, generativity, and sustainability frameworks, employing game-theoretic and empirical methodologies to analyze climate action, AI impacts, and cooperative behavior. Analysis of his 2024-2025 publications reveals intensifying focus on ecological transition mechanisms, including renewable energy communities, circular economy governance, and sustainable investment models. His work increasingly integrates climate economics with behavioral insights, examining conditional cooperation in environmental actions and AI's disruption of labor markets. No scientific awards or student advising information is publicly documented. He participates in departmental research initiatives including DEF Seminars and ROBO Seminars, contributing to the university's research centers on economic and social impact.

Huan Leiمشاهده پروفایل

استادیار

• Computational Mathematics
• Numerical Analysis
• Scientific Machine Learning
• +۴ مورد دیگر

Huan Lei is an Assistant Professor at Michigan State University, holding a joint appointment in the Department of Computational Mathematics, Science and Engineering and the Department of Statistics and Probability. He earned his Ph.D. in Applied Mathematics from Brown University in 2012 under George Karniadakis and a B.S. in Special Class for the Gifted Young from the University of Science & Technology of China in 2005. His research integrates scientific machine learning with numerical analysis to develop structure-preserving algorithms for partial and stochastic differential equations arising in multi-scale systems. His work spans multi-scale modeling , non-Markovian dynamics , coarse-grained molecular simulations , and data-driven parameterization . Recent publications focus on learning generalized Langevin equations with state-dependent memory, consensus-based free energy surfaces, and non-equilibrium coarse-grained models. His team applies these methods to fluid dynamics, biomolecular solvation, and climate systems. NSF CAREER Award (2021) Brown University Dissertation Fellowship (2012) He advises graduate and undergraduate researchers and seeks Ph.D. candidates with expertise in numerical analysis or scientific computing. His group receives funding from NSF, DOE, Ford, and MSU Foundation.

Kostas Papakonstantinouمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Civil Engineering
• Computational Mechanics
• +۹ مورد دیگر

Kostas Papakonstantinou is an Associate Professor in the Department of Civil Engineering at Penn State University, affiliated with the College of Engineering. His research bridges Artificial Intelligence (AI) with Civil Engineering, focusing on uncertainty quantification and decision-making under uncertainty. Research Areas: Uncertainty Quantification Stochastic Control Deep Reinforcement Learning Bayesian Analysis Nonlinear Filtering Computational Mechanics Infrastructure Management Rare Events Quantification His work emphasizes AI-driven solutions for structural life-cycle management, infrastructure systems, and autonomous operations. Funded by the NSF and USDOT, his projects include AI-enabled fiscally constrained life-cycle asset management and Deep reinforcement learning for multi-asset infrastructure management . Scientific Awards: NSF CAREER Award: Optimal engineering decision-making under uncertainties for enhanced structural life-cycle He teaches graduate courses on Uncertainty and Reliability in Civil Engineering (CE 566) and Computational Analysis of Randomness in Engineering (CE 597) .

Wayne A Fullerمشاهده پروفایل

استاد پژوهشی

• Survey Methodology
• Sampling Statistics
• Measurement Error Models
• +۴ مورد دیگر

Wayne A Fuller is a Research Professor at Iowa State University , specializing in survey methodology and sampling statistics. His work focuses on advanced techniques for handling missing data, small area estimation, and measurement error models, with applications to agricultural surveys and public health research. Education: PhD in Statistics from Iowa State University Research Interests: Fuller's work bridges theoretical and applied statistics through: Development of fractional hot deck imputation methods Bootstrap techniques for variance estimation Small area prediction under constrained models Measurement error correction in health and agricultural data Time series analysis with autoregressive components Integration of administrative data with survey samples Publication Trends: His recent work emphasizes computational approaches to small area estimation (2016-2025), including bootstrap prediction intervals and benchmarking techniques, alongside methodological advancements in imputation and measurement error correction (2004-2015). Contact: Email: waf@iastate.edu Phone: 515-294-5830 Location: Ames, Iowa

Hao Shenمشاهده پروفایل

استاد

• Stochastic Partial Differential Equations
• Quantum Field Theory
• Statistical Mechanics
• +۵ مورد دیگر

Hao Shen is a Professor in the Department of Mathematics at the University of Wisconsin-Madison. His academic work focuses on stochastic partial differential equations and their connections with quantum field theory, statistical mechanics, and geometric flows. Dr. Shen's educational background includes a PhD from Princeton University in 2013 under the supervision of Weinan E. He completed postdoctoral training at the University of Warwick with Martin Hairer (2014-2015) and served as a Ritt Assistant Professor at Columbia University with Ivan Corwin (2015-2018) before joining UW-Madison. Hao Shen's research interests span multiple areas of mathematical physics and probability theory: Stochastic partial differential equations (SPDEs) Quantum field theory, particularly Yang-Mills theory and gauge theories Statistical mechanics and interacting particle systems Geometric flows, including Ricci flow Stochastic quantization methods Renormalization theory for singular SPDEs His recent publications demonstrate a strong focus on the mathematical foundations of quantum field theories through stochastic methods, with particular attention to Yang-Mills theory in various dimensions and the O(N) sigma model. Shen's work often bridges rigorous mathematical analysis with physical applications, employing advanced techniques from regularity structures, paracontrolled distributions, and stochastic analysis. Simons Fellow in Mathematics (2024-2025) NSF CAREER DMS-2044415 (2021-2026) NSF DMS-1954091 (2020-2023) NSF DMS-1712684 / DMS-1909525 (2017-2020) As an academic leader, Dr. Shen serves on the editorial boards of prestigious journals including Annals of Probability, Stochastics and Partial Differential Equations: Analysis and Computations, and Annales de l'Institut Henri Poincaré. He actively contributes to the mathematical community through organizing conferences and summer schools, such as the 2024 MSRI/SLMath Summer Graduate School on "Stochastic quantization" and the upcoming 2025 SLMath semester program "Recent Trends in Stochastic Partial Differential Equations." Dr. Shen's teaching portfolio includes advanced courses in probability theory, stochastic analysis, and specialized topics in stochastic partial differential equations. His commitment to education is evident in his development of graduate-level courses that bridge theoretical mathematics with applications in physics.

Craig R. M. McKenzieمشاهده پروفایل

استاد

• Decision Making
• Rationality
• Judgment and Decision Making
• +۲ مورد دیگر

Craig R. M. McKenzie serves as Professor of Management and Psychology at UC San Diego's Rady School of Management, holding the Jerome Katzin Chair in Corporate Governance. His interdisciplinary work bridges business school research with psychological science, focusing on how environmental structures shape human judgment and decision processes. McKenzie earned his Ph.D. in Psychology from the University of Chicago (1994). His foundational research challenges conventional interpretations of cognitive biases by demonstrating how seemingly irrational choices often reflect reasonable Bayesian inferences given real-world constraints. Key contributions include reinterpreting the representativeness heuristic through environmental statistics and reframing framing effects as rational responses to contextual cues. His publication trajectory reveals evolving emphases from basic judgment mechanisms (2006-2015) toward moral decision architecture (2016-2023), with consistent exploration of probability sensitivity, default effects, and framing phenomena. Recent work increasingly examines moral dilemmas through computational modeling while maintaining experimental rigor in behavioral testing. Scientific recognition includes: National Science Foundation research awards Operations Research Society of America honors Society for Judgment and Decision Making distinctions McKenzie's grant activity centers on NSF-funded investigations of decision architecture, with collaborative projects spanning moral psychology, risk communication, and choice engineering. His theoretical frameworks directly inform behavioral interventions in healthcare, finance, and public policy domains through the Rady School's applied research initiatives. As a core faculty member in UC San Diego's Decision Science hub, he contributes to cross-departmental research networks examining cognitive foundations of economic behavior within the university's neuroscience and social science ecosystems.

John M. Hendersonمشاهده پروفایل

استاد

• Cognitive Psychology
• Visual Cognition
• Scene Perception
• +۳ مورد دیگر

John M. Henderson is a Distinguished Professor at the University of California, Davis , affiliated with the Visual Cognition Lab . He holds additional roles at the Center for Mind and Brain , Center for Vision Science , Center for Neuroscience , and Plasticity and Memory Program . As an editor for Collabra: Psychology and associate editor for Journal of Experimental Psychology: General , he contributes to open science and cognitive research dissemination. Ph.D. in Cognitive Psychology, University of Massachusetts, Amherst (1988) M.S. in Cognitive Psychology, University of Massachusetts, Amherst (1986) B.S. in Psychology, University of Massachusetts, Amherst (1983) Professor Henderson’s research investigates how visual information is acquired, recognized, and integrated into cognitive systems to guide behavior. His work combines scene perception , reading processes , and visual memory using eye tracking , fMRI , brain stimulation , and computational modeling . Recent studies explore semantic guidance of attention in natural scenes, neural correlates of fixation duration, and developmental attentional patterns. His 15 most recent publications (2023-2025) reflect a focus on semantic processing in visual cognition , scene perception , and computational modeling of attention . Topics include meaning-based attentional guidance, deep learning applications in scene analysis, and neural mechanisms of memory-guided eye movements. Collaborations span cognitive neuroscience, developmental psychology, and AI-driven scene understanding. Scientific honors include: Google Scholar Classics recognition (2017) for groundbreaking 2006 paper Fellow of the Association for Psychological Science, American Psychological Association, and Psychonomic Society Grants from the National Eye Institute and National Institute on Aging support his work on visual cognition and aging. His lab trains students in cognitive methods and interdisciplinary research, bridging psychology, neuroscience, and computational modeling.

Adam Elgaمشاهده پروفایل

استاد

• Philosophy of Probability
• Decision Theory
• Self-locating Belief
• +۴ مورد دیگر

Adam Elga serves as Professor of Philosophy and Director of the Program in Linguistics at Princeton University, based in the Department of Philosophy with his office in Laura Wooten Hall (204). His contact information includes email adame@princeton.edu and phone 609 258-1477, confirming active institutional engagement. Elga's research spans philosophy of probability, decision theory, self-locating belief, and existential risk, with notable intersections in linguistics through his program directorship. His work addresses fragmentation in belief systems, cognitive instability, and rational decision-making under uncertainty, contributing significantly to epistemological frameworks and philosophical probability models. Recent publications (2020-2025) demonstrate sustained focus on existential risk analysis, decision theory paradoxes, and belief fragmentation. His scholarship bridges theoretical philosophy with practical applications in risk management and cognitive science, particularly through investigations of suboptimal risk decisions and cognitive limitations in high-stakes scenarios. Scientific awards: None mentioned in provided text. Advising and grants: The scraped text contains no information about doctoral students, mentees, or specific research funding. As a full professor and program director, he likely oversees graduate research and secures institutional support, though concrete details are absent from the source material. Labs and research teams: No dedicated laboratories, research groups, or collaborative teams are referenced in the available documentation.

Yang Songمشاهده پروفایل

استادیار

• Generative Models
• Diffusion Models
• Score-Based Modeling
• +۵ مورد دیگر

Yang Song is an incoming Assistant Professor in Electrical Engineering and Computing and Mathematical Sciences at the California Institute of Technology (Caltech). Prior to joining Caltech, he leads the Strategic Explorations team at OpenAI. He received his Ph.D. in Computer Science from Stanford University under the supervision of Stefano Ermon and completed his Bachelor's degree in Mathematics and Physics from Tsinghua University. His educational background includes: Ph.D. in Computer Science, Stanford University Bachelor's in Mathematics and Physics, Tsinghua University Dr. Song's research focuses on building powerful AI models capable of understanding, generating, and reasoning with high-dimensional data across diverse modalities. He is particularly known for inventing foundational concepts and techniques in score-based diffusion models, which have revolutionized the field of generative AI. His work bridges theoretical advances with practical applications, particularly in image generation, medical imaging, and solving inverse problems. His research has demonstrated how score-based models can achieve state-of-the-art results in image generation while maintaining flexibility for various applications including medical image reconstruction. Analysis of his publication record reveals a strong trajectory in generative modeling, with a particular emphasis on score-based approaches and diffusion models. His work consistently addresses fundamental challenges in generative modeling including sample quality, training stability, computational efficiency, and application to real-world problems. His most recent work on consistency models represents a significant advancement toward making generative models practical for real-time applications. His notable achievements include: ICLR 2021 Outstanding Paper Award for Score-Based Generative Modeling through Stochastic Differential Equations NeurIPS 2021 Spotlight Presentation for Maximum Likelihood Training of Score-Based Diffusion Models Multiple ICLR Oral presentations for his work on consistency models Developing foundational techniques that power many modern AI image generation systems His GitHub repository for score-based generative modeling has gained significant traction in the research community, with over 1,700 stars, reflecting the impact of his work. His research bridges theoretical machine learning with practical applications, particularly in medical imaging where his techniques have shown promise for improving image reconstruction in CT and MRI.